Predicting the incidence and severity of wheat aphids and development of a web-enabled forewarning system in India

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dc.contributor.authorKumar, A.en_US
dc.contributor.authorSharma, R.en_US
dc.contributor.authorSingh, B.en_US
dc.contributor.authorPatil, S.D.en_US
dc.contributor.authorSrivastava, C.P.en_US
dc.contributor.authorSingh, G.P.en_US
dc.contributor.authorJoshi, A.K.en_US
dc.date.accessioned2024-11-30T11:59:22Z
dc.date.available2024-11-30T11:59:22Z
dc.date.issued2023-01
dc.description.abstractAim: Forecasting the incidence and severity of aphids, the major insect pest of wheat, is expected to significantly help in their management. In the present study, a set of weather-based models were developed to predict the timing and severity of Rhopalosiphum maidis infestation at Ludhiana falling under the North Western Plain Zone and R. padi at Niphad in the Peninsular Zone of India. Methodology: The weather indices-based regression models for two locations, Ludhiana and Niphad, were developed using the aphid population and weather data gathered over eight years (2006–14), and the models' predictive accuracy was successfully tested over four additional years (2014-18). The developed statistical models were transformed into three-tier architecture, web-based system, i.e. Presentation, application and data tier for dissemination of information. Results: The developed models can predict the crop’s age - when aphids first colonize the plants, when the aphid population attains the peak and the information about the peak intensity of the aphid population. For predicting the crop’s age at which population peaked at Ludhiana, the weighted interaction of the relative humidity (RH) in the evening and the number of hours of sunshine (NHS) along with the weighted interaction of minimum temperature and RH (morning) were important parameters while, at Niphad, the weighted NHS and the interaction of RH (morning and evening) were important. Likewise, for predicting the maximum aphid population at Ludhiana, the weighted interaction of minimum temperature and RH (morning) were important, while at Niphad, the key parameters were the weighted interaction of RH (evening) with the NHS. Interpretation: A prototype system developed to forecast the location-specific (Ludhiana and Niphad) infestation of wheat crops by aphids is expected to facilitate aphid management through an accurate forewarning at the locations.en_US
dc.identifier.affiliationsAgricultural Knowledge Management Unit, ICAR- Indian Agricultural Research Institute, New Delhi - 110 012, Indiaen_US
dc.identifier.affiliationsAgricultural Knowledge Management Unit, ICAR- Indian Agricultural Research Institute, New Delhi - 110 012, Indiaen_US
dc.identifier.affiliationsDepartment of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana - 141 004, Indiaen_US
dc.identifier.affiliationsAgricultural Research Station, Niphad, Mahatma Phule Krishi Vidyapeeth, Rahuri - 413 722, Indiaen_US
dc.identifier.affiliationsDepartment of Entomology and Agricultural Zoology, Banaras Hindu University, Varanasi - 221 005, Indiaen_US
dc.identifier.affiliationsIndian Institute of Wheat and Barley Research, Karnal - 132 001, Indiaen_US
dc.identifier.affiliationsInternational Maize and Wheat Improvement Center, New Delhi - 110 012, India, India; Borlaug Institute for South Asia, New Delhi - 110 012, Indiaen_US
dc.identifier.citationKumar A., Sharma R., Singh B., Patil S.D., Srivastava C.P., Singh G.P., Joshi A.K.. Predicting the incidence and severity of wheat aphids and development of a web-enabled forewarning system in India. Journal of Environmental Biology. 2023 Jan; 44(1): 83-90en_US
dc.identifier.issn0254-8704
dc.identifier.issn2394-0379
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/238472
dc.languageenen_US
dc.publisherTriveni Enterprisesen_US
dc.relation.issuenumber1en_US
dc.relation.volume44en_US
dc.source.urihttps://doi.org/10.22438/jeb/44/1/MRN-5058en_US
dc.subjectMean Absolute Percentage Erroren_US
dc.subjectRhopalosiphum maidisen_US
dc.subjectRhopalosiphum padien_US
dc.subjectThree-tier architectureen_US
dc.subjectWeather-based regression modelen_US
dc.titlePredicting the incidence and severity of wheat aphids and development of a web-enabled forewarning system in Indiaen_US
dc.typeJournal Articleen_US
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